DisCoveR: accurate and efficient discovery of declarative process models

Research output: Contribution to journalJournal articlepeer-review

Standard

DisCoveR : accurate and efficient discovery of declarative process models. / Back, Christoffer Olling; Slaats, Tijs; Hildebrandt, Thomas Troels; Marquard, Morten.

In: International Journal on Software Tools for Technology Transfer, Vol. 24, No. 4, 2022, p. 563–587.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Back, CO, Slaats, T, Hildebrandt, TT & Marquard, M 2022, 'DisCoveR: accurate and efficient discovery of declarative process models', International Journal on Software Tools for Technology Transfer, vol. 24, no. 4, pp. 563–587. https://doi.org/10.1007/s10009-021-00616-0

APA

Back, C. O., Slaats, T., Hildebrandt, T. T., & Marquard, M. (2022). DisCoveR: accurate and efficient discovery of declarative process models. International Journal on Software Tools for Technology Transfer, 24(4), 563–587. https://doi.org/10.1007/s10009-021-00616-0

Vancouver

Back CO, Slaats T, Hildebrandt TT, Marquard M. DisCoveR: accurate and efficient discovery of declarative process models. International Journal on Software Tools for Technology Transfer. 2022;24(4):563–587. https://doi.org/10.1007/s10009-021-00616-0

Author

Back, Christoffer Olling ; Slaats, Tijs ; Hildebrandt, Thomas Troels ; Marquard, Morten. / DisCoveR : accurate and efficient discovery of declarative process models. In: International Journal on Software Tools for Technology Transfer. 2022 ; Vol. 24, No. 4. pp. 563–587.

Bibtex

@article{47fce54687674db78b4bdcbc3b8f8406,
title = "DisCoveR: accurate and efficient discovery of declarative process models",
abstract = "Declarative process modeling formalisms—which capture high-level process constraints—have seen growing interest, especially for modeling flexible processes. This paper presents DisCoveR, an efficient and accurate declarative miner for learning Dynamic Condition Response (DCR) Graphs from event logs. We present a precise formalization of the algorithm, describe a highly efficient bit vector implementation and present a preliminary evaluation against five other miners, representing the state-of-the-art in declarative and imperative mining. DisCoveR performs competitively with each of these w.r.t. a fully automated binary classification task, achieving an average accuracy of 96.1% in the Process Discovery Contest 2019 (Results are available at https://icpmconference.org/2019/process-discovery-contest). We appeal to computational learning theory to gain insight into its performance as a classifier. Due to its linear time complexity, DisCoveR also achieves much faster run times than other declarative miners. Finally, we show how the miner has been integrated in a state-of-the-art declarative process modeling framework as a model recommendation tool and discuss how discovery can play an integral part of the modeling task and report on how the integration has improved the modeling experience of end-users.",
author = "Back, {Christoffer Olling} and Tijs Slaats and Hildebrandt, {Thomas Troels} and Morten Marquard",
year = "2022",
doi = "10.1007/s10009-021-00616-0",
language = "English",
volume = "24",
pages = "563–587",
journal = "Software-Concepts and Tools",
issn = "1433-2779",
publisher = "Springer",
number = "4",

}

RIS

TY - JOUR

T1 - DisCoveR

T2 - accurate and efficient discovery of declarative process models

AU - Back, Christoffer Olling

AU - Slaats, Tijs

AU - Hildebrandt, Thomas Troels

AU - Marquard, Morten

PY - 2022

Y1 - 2022

N2 - Declarative process modeling formalisms—which capture high-level process constraints—have seen growing interest, especially for modeling flexible processes. This paper presents DisCoveR, an efficient and accurate declarative miner for learning Dynamic Condition Response (DCR) Graphs from event logs. We present a precise formalization of the algorithm, describe a highly efficient bit vector implementation and present a preliminary evaluation against five other miners, representing the state-of-the-art in declarative and imperative mining. DisCoveR performs competitively with each of these w.r.t. a fully automated binary classification task, achieving an average accuracy of 96.1% in the Process Discovery Contest 2019 (Results are available at https://icpmconference.org/2019/process-discovery-contest). We appeal to computational learning theory to gain insight into its performance as a classifier. Due to its linear time complexity, DisCoveR also achieves much faster run times than other declarative miners. Finally, we show how the miner has been integrated in a state-of-the-art declarative process modeling framework as a model recommendation tool and discuss how discovery can play an integral part of the modeling task and report on how the integration has improved the modeling experience of end-users.

AB - Declarative process modeling formalisms—which capture high-level process constraints—have seen growing interest, especially for modeling flexible processes. This paper presents DisCoveR, an efficient and accurate declarative miner for learning Dynamic Condition Response (DCR) Graphs from event logs. We present a precise formalization of the algorithm, describe a highly efficient bit vector implementation and present a preliminary evaluation against five other miners, representing the state-of-the-art in declarative and imperative mining. DisCoveR performs competitively with each of these w.r.t. a fully automated binary classification task, achieving an average accuracy of 96.1% in the Process Discovery Contest 2019 (Results are available at https://icpmconference.org/2019/process-discovery-contest). We appeal to computational learning theory to gain insight into its performance as a classifier. Due to its linear time complexity, DisCoveR also achieves much faster run times than other declarative miners. Finally, we show how the miner has been integrated in a state-of-the-art declarative process modeling framework as a model recommendation tool and discuss how discovery can play an integral part of the modeling task and report on how the integration has improved the modeling experience of end-users.

U2 - 10.1007/s10009-021-00616-0

DO - 10.1007/s10009-021-00616-0

M3 - Journal article

VL - 24

SP - 563

EP - 587

JO - Software-Concepts and Tools

JF - Software-Concepts and Tools

SN - 1433-2779

IS - 4

ER -

ID: 276160734